Overview

Dataset statistics

Number of variables20
Number of observations3013
Missing cells5336
Missing cells (%)8.9%
Duplicate rows128
Duplicate rows (%)4.2%
Total size in memory1.2 MiB
Average record size in memory402.0 B

Variable types

Text2
Numeric10
Categorical8

Alerts

Dataset has 128 (4.2%) duplicate rowsDuplicates
Store_Room is highly imbalanced (70.3%)Imbalance
facing has 891 (29.6%) missing valuesMissing
Carpet_Area has 1195 (39.7%) missing valuesMissing
Built_Up_Area has 2071 (68.7%) missing valuesMissing
Super_BuiltUp_Area has 1080 (35.8%) missing valuesMissing
Carpet_Area is highly skewed (γ1 = 24.00048554)Skewed
Built_Up_Area is highly skewed (γ1 = 30.64806524)Skewed
floorNum has 136 (4.5%) zerosZeros
luxury_score has 465 (15.4%) zerosZeros

Reproduction

Analysis started2024-06-01 06:14:20.046843
Analysis finished2024-06-01 06:14:52.492411
Duration32.45 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct607
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size219.6 KiB
2024-06-01T11:44:53.005949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length17.572851
Min length1

Characters and Unicode

Total characters52947
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique268 ?
Unique (%)8.9%

Sample

1st rowmaa bhagwati residency
2nd rowApna Enclave
3rd rowTulsiani Easy in Homes
4th rowSmart World Orchard
5th rowParkwood Westend
ValueCountFrequency (%)
the 396
 
4.7%
park 212
 
2.5%
dlf 181
 
2.1%
global 164
 
1.9%
mm 156
 
1.8%
signature 154
 
1.8%
emaar 139
 
1.6%
heights 134
 
1.6%
godrej 121
 
1.4%
tulip 105
 
1.2%
Other values (674) 6673
79.1%
2024-06-01T11:44:54.124925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5699
 
10.8%
a 4861
 
9.2%
e 4759
 
9.0%
r 3732
 
7.0%
i 2848
 
5.4%
o 2346
 
4.4%
n 2302
 
4.3%
l 2252
 
4.3%
t 2159
 
4.1%
s 1977
 
3.7%
Other values (46) 20012
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37215
70.3%
Uppercase Letter 10019
 
18.9%
Space Separator 5699
 
10.8%
Other Punctuation 11
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4861
13.1%
e 4759
12.8%
r 3732
10.0%
i 2848
 
7.7%
o 2346
 
6.3%
n 2302
 
6.2%
l 2252
 
6.1%
t 2159
 
5.8%
s 1977
 
5.3%
h 1485
 
4.0%
Other values (16) 8494
22.8%
Uppercase Letter
ValueCountFrequency (%)
S 1273
12.7%
P 864
 
8.6%
T 849
 
8.5%
G 847
 
8.5%
A 677
 
6.8%
M 620
 
6.2%
V 598
 
6.0%
C 522
 
5.2%
H 512
 
5.1%
L 488
 
4.9%
Other values (16) 2769
27.6%
Other Punctuation
ValueCountFrequency (%)
, 10
90.9%
. 1
 
9.1%
Space Separator
ValueCountFrequency (%)
5699
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47234
89.2%
Common 5713
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4861
 
10.3%
e 4759
 
10.1%
r 3732
 
7.9%
i 2848
 
6.0%
o 2346
 
5.0%
n 2302
 
4.9%
l 2252
 
4.8%
t 2159
 
4.6%
s 1977
 
4.2%
h 1485
 
3.1%
Other values (42) 18513
39.2%
Common
ValueCountFrequency (%)
5699
99.8%
, 10
 
0.2%
- 3
 
0.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5699
 
10.8%
a 4861
 
9.2%
e 4759
 
9.0%
r 3732
 
7.0%
i 2848
 
5.4%
o 2346
 
4.4%
n 2302
 
4.3%
l 2252
 
4.3%
t 2159
 
4.1%
s 1977
 
3.7%
Other values (46) 20012
37.8%

price
Real number (ℝ)

Distinct438
Distinct (%)14.6%
Missing20
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean170.80975
Minimum16
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:44:54.533829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile35
Q190
median137
Q3203
95-th percentile425
Maximum1500
Range1484
Interquartile range (IQR)113

Descriptive statistics

Standard deviation139.89749
Coefficient of variation (CV)0.81902524
Kurtosis14.684263
Mean170.80975
Median Absolute Deviation (MAD)55
Skewness2.9677285
Sum511233.57
Variance19571.309
MonotonicityNot monotonic
2024-06-01T11:44:54.992635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 79
 
2.6%
110 61
 
2.0%
140 60
 
2.0%
150 59
 
2.0%
120 59
 
2.0%
90 58
 
1.9%
130 57
 
1.9%
95 53
 
1.8%
200 51
 
1.7%
175 47
 
1.6%
Other values (428) 2409
80.0%
ValueCountFrequency (%)
16 1
 
< 0.1%
17.5 1
 
< 0.1%
19 1
 
< 0.1%
20 8
0.3%
20.5 1
 
< 0.1%
21 6
0.2%
22 9
0.3%
23 1
 
< 0.1%
23.5 1
 
< 0.1%
24 5
0.2%
ValueCountFrequency (%)
1500 1
< 0.1%
1400 2
0.1%
1320 1
< 0.1%
1125 1
< 0.1%
1100 2
0.1%
1075 1
< 0.1%
1000 1
< 0.1%
995 1
< 0.1%
930 1
< 0.1%
920 1
< 0.1%

price_per_sqft
Real number (ℝ)

Distinct2129
Distinct (%)70.7%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9389.6453
Minimum4
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:44:55.489274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4641.5
Q16476
median8333
Q311102.5
95-th percentile16666
Maximum200000
Range199996
Interquartile range (IQR)4626.5

Descriptive statistics

Standard deviation6552.4691
Coefficient of variation (CV)0.69783989
Kurtosis477.13451
Mean9389.6453
Median Absolute Deviation (MAD)2132
Skewness17.077298
Sum28272222
Variance42934851
MonotonicityNot monotonic
2024-06-01T11:44:55.987940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 19
 
0.6%
8000 16
 
0.5%
12500 16
 
0.5%
5000 13
 
0.4%
8333 12
 
0.4%
6666 12
 
0.4%
7500 12
 
0.4%
6000 11
 
0.4%
8461 9
 
0.3%
7000 8
 
0.3%
Other values (2119) 2883
95.7%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
200000 2
0.1%
57507 1
< 0.1%
50000 1
< 0.1%
46917 1
< 0.1%
41666 1
< 0.1%
41245 2
0.1%
35483 1
< 0.1%
35222 1
< 0.1%
33209 1
< 0.1%
33198 2
0.1%

bedRoom
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.7992652
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:44:56.358982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78966731
Coefficient of variation (CV)0.28209807
Kurtosis0.021026014
Mean2.7992652
Median Absolute Deviation (MAD)1
Skewness0.12299297
Sum8381
Variance0.62357447
MonotonicityNot monotonic
2024-06-01T11:44:56.646213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1436
47.7%
2 942
31.3%
4 478
 
15.9%
1 104
 
3.5%
5 31
 
1.0%
6 3
 
0.1%
(Missing) 19
 
0.6%
ValueCountFrequency (%)
1 104
 
3.5%
2 942
31.3%
3 1436
47.7%
4 478
 
15.9%
5 31
 
1.0%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 31
 
1.0%
4 478
 
15.9%
3 1436
47.7%
2 942
31.3%
1 104
 
3.5%

bathroom
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.9512358
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:44:56.950398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0425111
Coefficient of variation (CV)0.35324562
Kurtosis0.17018977
Mean2.9512358
Median Absolute Deviation (MAD)1
Skewness0.58432537
Sum8836
Variance1.0868294
MonotonicityNot monotonic
2024-06-01T11:44:57.207679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1042
34.6%
3 988
32.8%
4 636
21.1%
5 169
 
5.6%
1 112
 
3.7%
6 42
 
1.4%
7 5
 
0.2%
(Missing) 19
 
0.6%
ValueCountFrequency (%)
1 112
 
3.7%
2 1042
34.6%
3 988
32.8%
4 636
21.1%
5 169
 
5.6%
6 42
 
1.4%
7 5
 
0.2%
ValueCountFrequency (%)
7 5
 
0.2%
6 42
 
1.4%
5 169
 
5.6%
4 636
21.1%
3 988
32.8%
2 1042
34.6%
1 112
 
3.7%

balcony
Categorical

Distinct4
Distinct (%)0.1%
Missing19
Missing (%)0.6%
Memory size176.7 KiB
3.0
1835 
2.0
749 
1.0
314 
0.0
 
96

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8982
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 1835
60.9%
2.0 749
24.9%
1.0 314
 
10.4%
0.0 96
 
3.2%
(Missing) 19
 
0.6%

Length

2024-06-01T11:44:57.569708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:44:57.876886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1835
61.3%
2.0 749
25.0%
1.0 314
 
10.5%
0.0 96
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 3090
34.4%
. 2994
33.3%
3 1835
20.4%
2 749
 
8.3%
1 314
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5988
66.7%
Other Punctuation 2994
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3090
51.6%
3 1835
30.6%
2 749
 
12.5%
1 314
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 2994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3090
34.4%
. 2994
33.3%
3 1835
20.4%
2 749
 
8.3%
1 314
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3090
34.4%
. 2994
33.3%
3 1835
20.4%
2 749
 
8.3%
1 314
 
3.5%

floorNum
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)1.4%
Missing20
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean7.9021049
Minimum0
Maximum45
Zeros136
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:44:58.197070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q311
95-th percentile19
Maximum45
Range45
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.2000495
Coefficient of variation (CV)0.78460734
Kurtosis3.2678425
Mean7.9021049
Median Absolute Deviation (MAD)4
Skewness1.3783428
Sum23651
Variance38.440614
MonotonicityNot monotonic
2024-06-01T11:44:58.611042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2 258
 
8.6%
3 248
 
8.2%
1 219
 
7.3%
4 203
 
6.7%
8 199
 
6.6%
7 188
 
6.2%
10 185
 
6.1%
6 185
 
6.1%
5 172
 
5.7%
9 171
 
5.7%
Other values (32) 965
32.0%
ValueCountFrequency (%)
0 136
4.5%
1 219
7.3%
2 258
8.6%
3 248
8.2%
4 203
6.7%
5 172
5.7%
6 185
6.1%
7 188
6.2%
8 199
6.6%
9 171
5.7%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 2
0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 3
0.1%
32 2
0.1%

facing
Categorical

MISSING 

Distinct8
Distinct (%)0.4%
Missing891
Missing (%)29.6%
Memory size188.3 KiB
North-East
505 
East
489 
North
301 
South
203 
West
183 
Other values (3)
441 

Length

Max length10
Median length5
Mean length6.9123468
Min length4

Characters and Unicode

Total characters14668
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowNorth-East
4th rowSouth-East
5th rowEast

Common Values

ValueCountFrequency (%)
North-East 505
16.8%
East 489
16.2%
North 301
 
10.0%
South 203
 
6.7%
West 183
 
6.1%
North-West 162
 
5.4%
South-East 144
 
4.8%
South-West 135
 
4.5%
(Missing) 891
29.6%

Length

2024-06-01T11:44:58.934178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:44:59.806813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
north-east 505
23.8%
east 489
23.0%
north 301
14.2%
south 203
9.6%
west 183
 
8.6%
north-west 162
 
7.6%
south-east 144
 
6.8%
south-west 135
 
6.4%

Most occurring characters

ValueCountFrequency (%)
t 3068
20.9%
s 1618
11.0%
o 1450
9.9%
h 1450
9.9%
E 1138
 
7.8%
a 1138
 
7.8%
N 968
 
6.6%
r 968
 
6.6%
- 946
 
6.4%
S 482
 
3.3%
Other values (3) 1442
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10654
72.6%
Uppercase Letter 3068
 
20.9%
Dash Punctuation 946
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3068
28.8%
s 1618
15.2%
o 1450
13.6%
h 1450
13.6%
a 1138
 
10.7%
r 968
 
9.1%
u 482
 
4.5%
e 480
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
E 1138
37.1%
N 968
31.6%
S 482
15.7%
W 480
15.6%
Dash Punctuation
ValueCountFrequency (%)
- 946
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13722
93.6%
Common 946
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3068
22.4%
s 1618
11.8%
o 1450
10.6%
h 1450
10.6%
E 1138
 
8.3%
a 1138
 
8.3%
N 968
 
7.1%
r 968
 
7.1%
S 482
 
3.5%
u 482
 
3.5%
Other values (2) 960
 
7.0%
Common
ValueCountFrequency (%)
- 946
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3068
20.9%
s 1618
11.0%
o 1450
9.9%
h 1450
9.9%
E 1138
 
7.8%
a 1138
 
7.8%
N 968
 
6.6%
r 968
 
6.6%
- 946
 
6.4%
S 482
 
3.3%
Other values (3) 1442
9.8%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size207.7 KiB
Relatively New
1476 
New Property
530 
Moderately Old
360 
Under Construction
274 
Undefined
236 

Length

Max length18
Median length14
Mean length13.529373
Min length9

Characters and Unicode

Total characters40764
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowOld Property
3rd rowNew Property
4th rowUnder Construction
5th rowUnder Construction

Common Values

ValueCountFrequency (%)
Relatively New 1476
49.0%
New Property 530
 
17.6%
Moderately Old 360
 
11.9%
Under Construction 274
 
9.1%
Undefined 236
 
7.8%
Old Property 137
 
4.5%

Length

2024-06-01T11:45:00.149930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:45:00.408204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2006
34.6%
relatively 1476
25.5%
property 667
 
11.5%
old 497
 
8.6%
moderately 360
 
6.2%
under 274
 
4.7%
construction 274
 
4.7%
undefined 236
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 7091
17.4%
l 3809
 
9.3%
t 3051
 
7.5%
2777
 
6.8%
y 2503
 
6.1%
r 2242
 
5.5%
N 2006
 
4.9%
w 2006
 
4.9%
i 1986
 
4.9%
a 1836
 
4.5%
Other values (15) 11457
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32197
79.0%
Uppercase Letter 5790
 
14.2%
Space Separator 2777
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7091
22.0%
l 3809
11.8%
t 3051
9.5%
y 2503
 
7.8%
r 2242
 
7.0%
w 2006
 
6.2%
i 1986
 
6.2%
a 1836
 
5.7%
d 1603
 
5.0%
o 1575
 
4.9%
Other values (7) 4495
14.0%
Uppercase Letter
ValueCountFrequency (%)
N 2006
34.6%
R 1476
25.5%
P 667
 
11.5%
U 510
 
8.8%
O 497
 
8.6%
M 360
 
6.2%
C 274
 
4.7%
Space Separator
ValueCountFrequency (%)
2777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37987
93.2%
Common 2777
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7091
18.7%
l 3809
 
10.0%
t 3051
 
8.0%
y 2503
 
6.6%
r 2242
 
5.9%
N 2006
 
5.3%
w 2006
 
5.3%
i 1986
 
5.2%
a 1836
 
4.8%
d 1603
 
4.2%
Other values (14) 9854
25.9%
Common
ValueCountFrequency (%)
2777
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7091
17.4%
l 3809
 
9.3%
t 3051
 
7.5%
2777
 
6.8%
y 2503
 
6.1%
r 2242
 
5.5%
N 2006
 
4.9%
w 2006
 
4.9%
i 1986
 
4.9%
a 1836
 
4.5%
Other values (15) 11457
28.1%

BHK
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7985397
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:45:00.719372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78977395
Coefficient of variation (CV)0.28220931
Kurtosis0.017500352
Mean2.7985397
Median Absolute Deviation (MAD)1
Skewness0.12213235
Sum8432
Variance0.62374289
MonotonicityNot monotonic
2024-06-01T11:45:01.082398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1444
47.9%
2 949
31.5%
4 481
 
16.0%
1 105
 
3.5%
5 31
 
1.0%
6 3
 
0.1%
ValueCountFrequency (%)
1 105
 
3.5%
2 949
31.5%
3 1444
47.9%
4 481
 
16.0%
5 31
 
1.0%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 31
 
1.0%
4 481
 
16.0%
3 1444
47.9%
2 949
31.5%
1 105
 
3.5%
Distinct186
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size219.0 KiB
2024-06-01T11:45:01.678807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length18
Mean length17.377365
Min length5

Characters and Unicode

Total characters52358
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)1.5%

Sample

1st row Krishna Colony
2nd row Ashok Vihar
3rd row Sohna
4th row Sector 61 Gurgaon
5th row Sector 92 Gurgaon
ValueCountFrequency (%)
sector 2565
29.9%
gurgaon 2550
29.7%
sohna 224
 
2.6%
102 113
 
1.3%
85 109
 
1.3%
92 100
 
1.2%
69 92
 
1.1%
90 90
 
1.0%
65 89
 
1.0%
81 87
 
1.0%
Other values (193) 2564
29.9%
2024-06-01T11:45:02.718055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8582
16.4%
o 5509
10.5%
r 5413
10.3%
a 3297
 
6.3%
n 2918
 
5.6%
S 2862
 
5.5%
e 2771
 
5.3%
t 2704
 
5.2%
c 2633
 
5.0%
u 2618
 
5.0%
Other values (47) 13051
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31581
60.3%
Space Separator 8582
 
16.4%
Uppercase Letter 6369
 
12.2%
Decimal Number 5763
 
11.0%
Dash Punctuation 57
 
0.1%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5509
17.4%
r 5413
17.1%
a 3297
10.4%
n 2918
9.2%
e 2771
8.8%
t 2704
8.6%
c 2633
8.3%
u 2618
8.3%
g 2567
8.1%
h 403
 
1.3%
Other values (14) 748
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
S 2862
44.9%
G 2574
40.4%
A 237
 
3.7%
D 112
 
1.8%
C 107
 
1.7%
P 107
 
1.7%
V 67
 
1.1%
M 55
 
0.9%
L 54
 
0.8%
N 46
 
0.7%
Other values (10) 148
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 896
15.5%
9 727
12.6%
8 695
12.1%
0 661
11.5%
6 611
10.6%
7 596
10.3%
3 507
8.8%
5 410
7.1%
2 407
7.1%
4 253
 
4.4%
Space Separator
ValueCountFrequency (%)
8582
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37950
72.5%
Common 14408
 
27.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5509
14.5%
r 5413
14.3%
a 3297
8.7%
n 2918
7.7%
S 2862
7.5%
e 2771
7.3%
t 2704
7.1%
c 2633
6.9%
u 2618
6.9%
G 2574
6.8%
Other values (34) 4651
12.3%
Common
ValueCountFrequency (%)
8582
59.6%
1 896
 
6.2%
9 727
 
5.0%
8 695
 
4.8%
0 661
 
4.6%
6 611
 
4.2%
7 596
 
4.1%
3 507
 
3.5%
5 410
 
2.8%
2 407
 
2.8%
Other values (3) 316
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8582
16.4%
o 5509
10.5%
r 5413
10.3%
a 3297
 
6.3%
n 2918
 
5.6%
S 2862
 
5.5%
e 2771
 
5.3%
t 2704
 
5.2%
c 2633
 
5.0%
u 2618
 
5.0%
Other values (47) 13051
24.9%

Servant_Room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.8 KiB
0
2013 
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3013
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2013
66.8%
1 1000
33.2%

Length

2024-06-01T11:45:03.099005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:45:03.467022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2013
66.8%
1 1000
33.2%

Most occurring characters

ValueCountFrequency (%)
0 2013
66.8%
1 1000
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3013
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2013
66.8%
1 1000
33.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2013
66.8%
1 1000
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2013
66.8%
1 1000
33.2%

Study_Room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.8 KiB
0
2532 
1
481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3013
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2532
84.0%
1 481
 
16.0%

Length

2024-06-01T11:45:03.909837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:45:04.272866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2532
84.0%
1 481
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 2532
84.0%
1 481
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3013
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2532
84.0%
1 481
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2532
84.0%
1 481
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2532
84.0%
1 481
 
16.0%

Pooja_Room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.8 KiB
0
2651 
1
362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3013
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2651
88.0%
1 362
 
12.0%

Length

2024-06-01T11:45:04.576057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:45:04.872264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2651
88.0%
1 362
 
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2651
88.0%
1 362
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3013
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2651
88.0%
1 362
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2651
88.0%
1 362
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2651
88.0%
1 362
 
12.0%

Store_Room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.8 KiB
0
2855 
1
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3013
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2855
94.8%
1 158
 
5.2%

Length

2024-06-01T11:45:05.149521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:45:05.424783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2855
94.8%
1 158
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 2855
94.8%
1 158
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3013
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2855
94.8%
1 158
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2855
94.8%
1 158
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2855
94.8%
1 158
 
5.2%

Carpet_Area
Real number (ℝ)

MISSING  SKEWED 

Distinct689
Distinct (%)37.9%
Missing1195
Missing (%)39.7%
Infinite0
Infinite (%)0.0%
Mean2546.6568
Minimum66
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:45:05.733956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile481
Q1876.75
median1300
Q31764.7975
95-th percentile2800
Maximum607936
Range607870
Interquartile range (IQR)888.0475

Descriptive statistics

Standard deviation23128.91
Coefficient of variation (CV)9.0820682
Kurtosis587.72587
Mean2546.6568
Median Absolute Deviation (MAD)450
Skewness24.000486
Sum4629822
Variance5.349465 × 108
MonotonicityNot monotonic
2024-06-01T11:45:06.109950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 40
 
1.3%
1600 35
 
1.2%
1800 33
 
1.1%
1200 31
 
1.0%
1500 29
 
1.0%
1350 28
 
0.9%
1650 26
 
0.9%
1300 22
 
0.7%
2000 21
 
0.7%
1000 21
 
0.7%
Other values (679) 1532
50.8%
(Missing) 1195
39.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 2
0.1%
77.53 1
 
< 0.1%
84.01 1
 
< 0.1%
86 2
0.1%
92 3
0.1%
92.44 1
 
< 0.1%
100 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

Built_Up_Area
Real number (ℝ)

MISSING  SKEWED 

Distinct393
Distinct (%)41.7%
Missing2071
Missing (%)68.7%
Infinite0
Infinite (%)0.0%
Mean2489.2199
Minimum97
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:45:06.557787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile658.55
Q11275.1325
median1615.5
Q32000
95-th percentile3142.9
Maximum737147
Range737050
Interquartile range (IQR)724.8675

Descriptive statistics

Standard deviation23973.316
Coefficient of variation (CV)9.6308552
Kurtosis940.19829
Mean2489.2199
Median Absolute Deviation (MAD)375.5
Skewness30.648065
Sum2344845.1
Variance5.7471988 × 108
MonotonicityNot monotonic
2024-06-01T11:45:07.109312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1900 31
 
1.0%
1600 24
 
0.8%
1300 23
 
0.8%
2000 21
 
0.7%
1700 20
 
0.7%
1350 17
 
0.6%
1800 16
 
0.5%
2200 15
 
0.5%
2600 14
 
0.5%
1500 14
 
0.5%
Other values (383) 747
 
24.8%
(Missing) 2071
68.7%
ValueCountFrequency (%)
97 2
0.1%
118 1
< 0.1%
129.42 1
< 0.1%
140 1
< 0.1%
300 1
< 0.1%
301 2
0.1%
318 1
< 0.1%
425 1
< 0.1%
430 1
< 0.1%
450 2
0.1%
ValueCountFrequency (%)
737147 1
< 0.1%
5350 1
< 0.1%
5200 2
0.1%
5010 1
< 0.1%
5000 1
< 0.1%
4800 1
< 0.1%
4630 1
< 0.1%
4600 1
< 0.1%
4500 1
< 0.1%
4200 1
< 0.1%

Super_BuiltUp_Area
Real number (ℝ)

MISSING 

Distinct599
Distinct (%)31.0%
Missing1080
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean1917.7975
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:45:07.731616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile748
Q11457
median1825
Q32215
95-th percentile3187.8
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation768.43168
Coefficient of variation (CV)0.40068447
Kurtosis9.9399156
Mean1917.7975
Median Absolute Deviation (MAD)375
Skewness1.8047182
Sum3707102.6
Variance590487.25
MonotonicityNot monotonic
2024-06-01T11:45:08.349961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 38
 
1.3%
1950 38
 
1.3%
2000 26
 
0.9%
1578 25
 
0.8%
2150 23
 
0.8%
1640 22
 
0.7%
2408 20
 
0.7%
1900 19
 
0.6%
1350 19
 
0.6%
1930 18
 
0.6%
Other values (589) 1685
55.9%
(Missing) 1080
35.8%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 2
0.1%
4848 2
0.1%

luxury_score
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.913707
Minimum0
Maximum62
Zeros465
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-06-01T11:45:08.833667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median28
Q353
95-th percentile62
Maximum62
Range62
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.402048
Coefficient of variation (CV)0.63928792
Kurtosis-1.1361051
Mean31.913707
Median Absolute Deviation (MAD)19
Skewness-0.11103683
Sum96156
Variance416.24355
MonotonicityNot monotonic
2024-06-01T11:45:09.650485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 465
15.4%
62 405
13.4%
28 389
12.9%
53 349
 
11.6%
47 171
 
5.7%
34 88
 
2.9%
20 72
 
2.4%
37 72
 
2.4%
27 53
 
1.8%
15 51
 
1.7%
Other values (41) 898
29.8%
ValueCountFrequency (%)
0 465
15.4%
6 17
 
0.6%
7 43
 
1.4%
8 34
 
1.1%
9 48
 
1.6%
10 12
 
0.4%
12 1
 
< 0.1%
13 31
 
1.0%
14 20
 
0.7%
15 51
 
1.7%
ValueCountFrequency (%)
62 405
13.4%
56 11
 
0.4%
55 2
 
0.1%
54 3
 
0.1%
53 349
11.6%
52 11
 
0.4%
50 1
 
< 0.1%
49 6
 
0.2%
48 1
 
< 0.1%
47 171
5.7%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.8 KiB
0
2030 
1
826 
2
 
157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3013
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2030
67.4%
1 826
27.4%
2 157
 
5.2%

Length

2024-06-01T11:45:10.197019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T11:45:10.572016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2030
67.4%
1 826
27.4%
2 157
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 2030
67.4%
1 826
27.4%
2 157
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3013
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2030
67.4%
1 826
27.4%
2 157
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2030
67.4%
1 826
27.4%
2 157
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2030
67.4%
1 826
27.4%
2 157
 
5.2%

Interactions

2024-06-01T11:44:46.825569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:21.496281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:24.281833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:28.114581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:30.857244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:33.602935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:36.109240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:38.400223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:40.789783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:44.067051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:47.202561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:21.776534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:24.561084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:28.383859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:31.160433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:33.848243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:36.344570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:38.651550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:41.211689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:44.291419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:47.580548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:22.087701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:24.987945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:28.654140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:31.482573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:34.154428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:36.591910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:38.878942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:41.568698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:44.576653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:47.903685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:22.334042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:25.275174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:28.895497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:31.746866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:34.381817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:36.816633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:39.117305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:41.904833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:44.839949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:48.210865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:22.613294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:25.687078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:29.164774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:32.015145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:34.626167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:37.034876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:39.356664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:42.199013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:45.109262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:48.475155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:22.854648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:26.046116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:29.386181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:32.307365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:34.840623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:37.238331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:39.573085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:42.542094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:45.333627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:48.806271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:23.126956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:26.774165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:29.648479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:32.596593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:35.080948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:37.445776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:39.805463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:43.006850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:45.662782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:49.156334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:23.478981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:27.116251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:29.895819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:32.844927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:35.324296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:37.670176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:40.042830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:43.255187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:45.983891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:49.520364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:23.795133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:27.556074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:30.228926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:33.119198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:35.659403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:37.942447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:40.277153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:43.564360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:46.200241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:49.779669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:24.038482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:27.818372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:30.576993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:33.362577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:35.881807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:38.176821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:40.518541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:43.797734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-01T11:44:46.522384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-06-01T11:44:50.313239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-01T11:44:51.312566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-01T11:44:52.022673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

societypriceprice_per_sqftbedRoombathroombalconyfloorNumfacingagePossessionBHKLocationServant_RoomStudy_RoomPooja_RoomStore_RoomCarpet_AreaBuilt_Up_AreaSuper_BuiltUp_Arealuxury_scorefurnishing_type
0maa bhagwati residency45.05000.02.02.01.04.0WestRelatively New2Krishna Colony0000900.0NaNNaN130
1Apna Enclave50.07692.02.02.01.01.0WestOld Property2Ashok Vihar0000650.0NaNNaN201
2Tulsiani Easy in Homes40.06722.02.02.03.012.0NaNNew Property2Sohna0000595.0NaNNaN170
3Smart World Orchard147.012250.02.02.02.02.0NaNUnder Construction2Sector 61 Gurgaon01001200.0NaNNaN290
4Parkwood Westend70.05204.02.02.03.05.0NaNUnder Construction2Sector 92 Gurgaon0100NaNNaN1345.000
5Signature Global Infinity Mall41.06269.02.02.03.03.0NaNUndefined2Sector 36 Gurgaon0000NaN654.0NaN00
6The Cocoon200.013333.03.03.03.05.0NaNNew Property3Dwarka Expressway Gurgaon0000NaNNaN1500.000
7ATS Triumph180.07860.03.04.03.014.0NaNNew Property3Sector 104 Gurgaon00002290.0NaNNaN240
8Vatika XpreSS The Leafions110.08148.02.04.03.02.0North-EastUnder Construction2Sector 88B Gurgaon01001050.01350.0NaN240
9Raheja Revanta475.016885.03.03.02.031.0NaNUnder Construction3Sector 78 Gurgaon1000NaN2813.0NaN370
societypriceprice_per_sqftbedRoombathroombalconyfloorNumfacingagePossessionBHKLocationServant_RoomStudy_RoomPooja_RoomStore_RoomCarpet_AreaBuilt_Up_AreaSuper_BuiltUp_Arealuxury_scorefurnishing_type
3003Palam Vihar Society40.08602.01.01.01.05.0NorthModerately Old1Palam Vihar0000425.0450.0465.0232
3004MM Woodshire140.05929.03.04.00.01.0EastNew Property3Sector 107 Gurgaon0000NaNNaN2361.0340
3005Krishna appartment35.03500.03.03.01.02.0NaNOld Property3Dharam Colony0000NaNNaN1000.000
3006Spire Woods Now Ananda by Alpha corp125.06902.03.04.03.011.0NaNUnder Construction3Sector 103 Gurgaon11001811.0NaNNaN490
3007DLF Regency Park135.011992.02.02.02.01.0NaNModerately Old2DLF Phase 40000NaN1109.0NaN00
3008Ansal Heights105.05541.03.03.03.09.0North-EastUnder Construction3Sector 86 Gurgaon1000NaNNaN1895.000
3009Parsvnath Green Ville330.09984.05.05.03.04.0NaNOld Property5Sector 48 Gurgaon1000NaN3305.03905.0320
3010Raheja Vedaanta95.05214.03.03.03.03.0NaNRelatively New3Sector 108 Gurgaon0000NaNNaN1822.0470
3011Ambience Lagoon580.012500.03.04.03.09.0North-EastOld Property3DLF Phase 31110NaN3700.0NaN282
3012DLF The Crest1100.035222.04.06.03.07.0NaNRelatively New4Sector 54 Gurgaon1000NaNNaN3123.0522

Duplicate rows

Most frequently occurring

societypriceprice_per_sqftbedRoombathroombalconyfloorNumfacingagePossessionBHKLocationServant_RoomStudy_RoomPooja_RoomStore_RoomCarpet_AreaBuilt_Up_AreaSuper_BuiltUp_Arealuxury_scorefurnishing_type# duplicates
0ASS The Leafotech Blith92.06739.02.02.03.022.0NaNUnder Construction2Sector 99 Gurgaon0000NaNNaN1365.02602
1ASS The Leafotech Blith190.06702.04.04.03.02.0North-EastUndefined4Sector 99 Gurgaon0000NaN2835.0NaN002
2ATS Tourmaline230.08897.03.04.03.010.0EastNew Property3Sector 109 Gurgaon1000NaNNaN2585.04002
3ATS Triumph200.08733.03.04.03.04.0NaNRelatively New3Sector 104 Gurgaon1000NaNNaN2290.02402
4AVL Gurgaon75.07500.02.02.02.05.0North-EastRelatively New2Sector 36A Gurgaon0000727.0NaN1000.01502
5Ambience Caitriona1400.0200000.04.05.03.03.0EastUndefined4Sector 24 Gurgaon0000NaN700.0NaN002
6Ansal Heights90.05325.03.03.02.010.0NaNNew Property3Sector 86 Gurgaon0000NaN1690.0NaN2202
7Ansal Heights130.04666.04.06.02.011.0EastNew Property4Sector 86 Gurgaon1000NaNNaN2786.03302
8Ansal Housing Highland Park87.56429.02.02.03.03.0NaNNew Property2Sector 103 Gurgaon0000NaNNaN1361.03302
9Antriksh Heights85.05556.02.02.03.010.0North-WestNew Property2Sector 84 Gurgaon0100NaNNaN1350.0902